Innovative solutions for enterprise-wide collateral management and optimization. White Paper Collateral optimization how it really works

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1 APEX collateral Innovative solutions for enterprise-wide collateral management and optimization White Paper Collateral optimization how it really works

2 Contents 1 Introduction 1 What is optimization? 2 What approaches are available? 2 Top up optimization vs collateral re-balancing 3 What does optimal mean? 3 Optimization cost models 5 Collateral allocation methods 6 Choosing a path Ted Allen Vice President Capital Markets Collateral Ed Hellaby Product Management Collateral Management & Optimization

3 Introduction Over the past eighteen months the market has seen a huge rise in the number of people talking about collateral optimization. Not an industry paper, panel discussion or conference goes by without devoting attention to the topic. The drivers behind why collateral optimization is so in vogue are clear and well documented. It has become a truism to state that collateral optimization is essential to deal with the twin problems of scarcity of high quality liquid assets and the overall increasing demands for collateral resulting from the various strands of regulations. Many industry experts discuss bringing together silos and the importance of establishing a global inventory and we agree wholeheartedly with inventory management as a first step down the road to optimization. However, it s rare to find information about a pragmatic approach to how to make the next step and institutionalize a collateral optimization program. This paper aims to demystify the seemingly complex solutions that exist within the collateral optimization space, providing insight into how the solutions work and the pitfalls to avoid when building a solution or choosing a partner vendor. The cost savings from choosing the right approach can be substantial, but you also need an approach that is achievable. This paper should be of interest to institutions on both the sell side and the buy side as well as collateral service providers that are investing in a collateral optimization program. It should also be of interest to those using or planning to use a service provider to manage the collateral requirements. These service providers increasingly play on optimization and this paper should help in an evaluation of their capabilities. What is collateral optimization? The industry has used the banner of collateral optimization to mean many things to many people. So let s take a start by listing some different perspectives: For the Front Office minimizing the collateral funding charge that hits trading activity. For the Treasury making the most efficient use of assets to meet requirements for collateral, for liquidity, for regulatory capital etc. whilst minimizing the balance sheet impact. For Operations efficiency and automation of daily processes to handle the increased velocity of collateral assets from the optimization process. For the Buy Side assuaging the impact of new regulations by minimizing the impact of higher collateral requirements. For the Service Provider providing a differentiator from competitors to win or retain client custody business and as a gateway to additional revenue generating activities such as collateral upgrades or transformations. These are problems of complexity on the one hand with the use of complex algorithms we will look at later. On the other hand they are problems of scale in automating the collateral allocation decision making and execution. An effective collateral optimization program should aim to address both of these and more. At its heart, the objective of the optimization problem is to achieve the optimal allocation of assets against requirements whilst satisfying a set of constraints. These constraints may be hard rules (for example eligibility, re-hypothecation rules or concentration limits) or other soft factors such as operational limits on the number of movements you can physically process. To move the question forward, we must specify a pragmatic definition of optimal and the techniques we can use to get us to such a solution. 1

4 What approaches are available? One of the reasons we have had such little industry press activity focus on the how is because optimization is an unknown domain for many collateral practitioners. The approach undertaken by many in the past has been manually intensive and usually involved ranking of assets based on a firm s preference and a waterfall allocation to requirements. However, in a world of increasing collateral requirements and a larger number of collateral pools, the process of optimization becomes one that increases in complexity as volumes grow. Let s start with first principles. We have a number of assets of varying quantity, worth and quality that we can allocate against a set of collateral requirements. This can be illustrated quite simply below. We can see that despite the constraints on the solution such as eligibility and concentration limits, if we consider potentially hundreds or thousands of assets and requirements, there will still be a huge number of different combinations of how our set of assets could be allocated against the different requirements. One particular bond might be eligible against a number of requirements, selecting which is the best is simple when looked at in isolation (it might be the one with the best haircut for example). However in the context of considering all the other available assets in the inventory (real or theoretic) and all of the requirements, we see that the optimal solution quickly becomes unmanageably difficult to calculate without recourse to sophisticated tools. The collateral optimization challenge is to identify which of the combinations is the most optimal. Figure 1. The collateral allocation problem Available assets Asset 1 Asset 2 Asset 3 Allocations to calls Call A Call B Call C Call D Unallocated Allocated asset 1 Allocated asset 2 Cost + + cost cost Allocated asset 3 cost Top up optimization vs collateral re-balancing The process of allocating collateral against requirements has traditionally been performed as calls are received or new requirements calculated. We may look at a preference ranking of potential assets to post and perhaps also take into account the haircuts that would be applied. The selected assets would usually be left with the counterparty until the requirement went away or there was some other activity (the asset went special, a pending corporate action or a sell on the position for example). Whilst this process will certainly yield a more optimal allocation of collateral than simply allocating an arbitrary bond, just considering top ups and new requirements won t get you optimized. Additional efficiencies can be realized by a collateral optimization process that considers not just new collateral postings, but also the inventory that we have already posted out to other requirements to see if assets could be more effectively redeployed elsewhere. By rebalancing all of our collateral postings regularly, we can ensure that collateral is redeployed if it can be better allocated to other requirements or to fulfil other purposes. Of course, in order to consider a collateral rebalancing strategy, an institution s optimization and collateral operations infrastructure must be able to facilitate sufficient levels of 2 Collateral optimization how it really works

5 automation and straight through processing to deal with the increased volumes of movements. Active substitutions and high collateral velocity have long been a characteristic of tri-party repo offerings. Market infrastructure improvements such as electronic messaging and the hook ups we are seeing between the CSDs and global custodians cutting down settlement times mean that such rebalancing can now be achieved to a greater degree in the bilateral world. The difference here of course is that firms themselves can in theory decide on the rebalancing and instigate the movements rather than relying on the tri-party agent to do it for them. What does optimal mean? The question of what we mean by the optimal allocation of the assets seems fairly trivial when first considered. However, once you sit down and try to define it for your firm it can quickly throw up complications. Firms that are long eligible non-cash collateral may wish to pledge out the assets with the lowest liquidity; corporates or those that are long cash may wish to post cash where an agreement will pay more interest than they can otherwise get on that cash; there may be credit constraints to be considered for others. They may well want to match the funding of the collateral to the expected duration of the requirement. Determining what we mean by optimal allocation of the assets is how we start to define the cost model. The cost model sits at the heart of the optimization algorithm. Firms should ideally always look at the alternate potential uses of an asset. This way they can measure not just the funding cost of use of the asset as collateral, but also the opportunity cost. For example, an asset may be deployed in a lending program and generate income. In almost all cases there will be subtle differences between the objectives of each firm s optimization program. The inputs to the rules will not be static but will change as the firm s inventory and exposure profile changes and with the prevailing market and credit environment. Once we have defined what we mean by an optimal allocation of the assets, we can go about codifying this in a set of rules. These rules form the cost model of the objective function of an optimization solution and are arguably the most important part of the process. A successful cost model is the keystone of your optimization infrastructure. When your cost model is well defined, then you can look at the allocation process that the cost model drives. Let s now look at some of the models in use today. Optimization cost models The optimization cost model is what firms use to define how desirable they consider an asset and thus how it should be treated in the collateral allocation process itself. There are a number of different options open to us as to how we go about defining the cost model. This can range from the blunt and simple to the sharp and accurate. Preference ranking cost models Perhaps the simplest approach to optimization where many institutions begin their thought process is to establish a ranking of their inventory. They would prefer to post out those assets that are lower ranked and retain those assets that are higher ranked. In its most basic form a collateral optimization solution should facilitate the ability to capture and define a preference order manually. The definition of these ranking rules will vary based on an institution s optimization strategy, but may take into consideration a combination of credit quality, liquidity profiles, maturity, rating, asset classes or market segments. By formulating a weighting against the various attributes, you can rank the relative importance of each. These rankings will change over time with the firm s inventory profile, risk appetite and economic outlook and there should be a mechanism for keeping these up to date. Figure 2. Optimization infrastructure Inventory Agreement terms Market data Requirements Data aggregation layer Workflow & user interface Cost model 3

6 Market based rankings Whilst a simplistic preference based optimization is certainly more optimal than having no program whatsoever, it doesn t work particularly well in many circumstances. One such issue is that the ranking assigned to an asset must be continually updated to ensure that the logic behind the preferences is sound. A smart way to enhance a firm s ranking strategy is to use tools such as SunGard s Astec Analytics Borrower Activity Rating (BAR). BAR is a daily rating of how desirable an asset is within the lending market based on broad data including cost, volumes and utilization levels. This identifies the hottest stocks and can feed directly into the preferences used by an automated allocation process. Such a measure can ensure the most desirable collateral is retained without the need to manually maintain rankings, for example as assets become more or less special. A ranked optimization approach is one the market feels comfortable adopting as it yields a result that is easily explainable, intuitive (I prefer to post a low ranked corporate bond to a US treasury) and is simple to implement. However it is a fairly blunt tool and there are many downsides involved in a preference ranking cost model that limit the effectiveness of an optimization using this approach. As an example of the problem, there is no easy way of taking into account haircuts in the allocation process if you use preference rankings. If I always prefer to retain assets in bucket 1 and post assets in bucket 2, what happens if the haircuts for assets in bucket 2 are double those in bucket 1 for a specific requirement? I would have to post twice as much in terms of market value. How do we capture the additional credit risk this engenders or consider the opportunity cost of the income we ve foregone on that asset by using it as collateral? At what point does posting a greater amount of the asset in bucket 2 become worse than a smaller amount of the asset in bucket 1? This problem becomes more complex when we consider other operational factors. Take for example the fact that I have already posted a position to a requirement, it may be the case that I would prefer to post more of the same position to a requirement even if it means posting something that is not the lowest ranked. We can ask similar questions around the operational costs and risk involved in substitutions and other economic factors that a preference ranking cost model cannot easily consider. A preference ranking can work in some circumstances, but given the shortcomings, you may want to consider other options. Economic cost based models One alternative to preference ranking of assets by buckets is to define the desirability of an asset based on the economic cost of the use of that asset as collateral. The economic cost model can calculate the full cost of placing each position against a given requirement, whilst also taking into consideration the haircut that will be applied. Defining this cost of course is not a trivial exercise and is something that will be individual to each institution. One important element can be perhaps measured as the spread between the return on the asset in the repo market compared to your internal funding desk. Further considerations in the cost model will include defining the relative values of different assets classes, bonds, equities and cash. Firms must also take into account their own circumstances and balance sheet when defining the model. For example, it may prefer to offload a certain set of assets into the collateral market due to RWA or concentration risk reasons. Capturing the operational cost of moving a piece of collateral is also important. This may be comprised of internal operational costs and external costs and may differ based on depository or custodian and the market. These costs impact incoming and outgoing flows and may be an important consideration along with settlement risk in the relative desirability of substitutions. This model could also be weighted to consider the fact that I have already posted an asset to a requirement, it may be the case that I would prefer to post more of the same position to that requirement if it increases (top-up) even if it means posting something that is not the highest ranked. There are many factors at play in defining the right cost model for a firm and it may well even vary within the firm depending on the portion of the inventory being used, but a well-defined and tailored economic cost model allows us to replace a process previously driven by tacit behaviors to one driven by sound financial logic tailored to the circumstances and outlook of each institution. Once you have defined the cost model to be used, an allocation engine is then employed to assign the collateral to the requirements. The next section discusses options for allocation methods. 4 Collateral optimization how it really works

7 Collateral allocation methods With the cost model defined, the next step in building a collateral optimization program is to create the asset allocation method. This is the process that defines how to decide which collateral should be posted to which requirement. As with the cost model, there are methods of varying degrees of sophistication that can be employed. Waterfall allocation The instinctive approach many collateral managers have taken in the past to allocation of collateral to requirements is a waterfall or sequential allocation. Indeed this is the method used in some tri-party repo collateral optimization models. Inherently simplistic, we would not regard it as true optimization, however pragmatists may argue that it is good enough where access to quantitative resources are not available. Just as we discussed ranking assets in preference buckets, a waterfall allocation will have a similar ranking of the collateral requirements. The waterfall allocation will iterate through the requirements based on the defined order and then allocate the worst assets first to the highest ranked agreements using the defined cost model. Through sequentially processing collateral calls posting out the least desirable positions first, firms can reach a better collateral allocation than would otherwise be the case. Some will also attempt this approach for a rebalancing of the collateral posted as well as for top-ups or new collateral requirements. This approach is quite simple to implement and doesn t need an optimization algorithm to run, simply a rule set to iterate through the requirements allocating cheapest to deliver collateral. It will certainly deliver some of the benefits of a true optimization and result in more efficient allocation than a random approach and may be good enough for firms with a relatively narrow inventory. Allocation using numerical optimizations To effectively consider all of the parameters of the problem we must move away from a sequential approach to optimization and instead consider all of the variables in one single process. The mathematical technique that allows us to do this is called numerical optimization. This tranche of linear mathematics allows us to simultaneously consider millions of variables and arrive at the most optimal allocation of collateral inventory whilst taking into consideration factors such as haircuts, lot sizes, concentration limits and eligibility. There are a number of standard optimization algorithms that can be used to solve the optimization problem. Some are more suitable than others and in our experience, the selection of the right algorithm and how the business problem is translated into a set of inputs to the algorithm require careful consideration. A poorly phrased calibration can lead to unacceptable run times and technical expertise can help find the approach that will give an answer that is good enough, quick enough. Building a tool to perform such a numerical optimization combined with a tailored cost model is not a trivial exercise and in our experience needs significant quantitative resources throwing at the problem. A numerical mathematical approach will require deep knowledge of the linear mathematics and non-linear techniques combined with core collateral management knowledge to deliver an efficient solution. Some institutions will prefer to partner with a vendor who has already made that investment and built such a sophisticated solution if they do not have the time, money or resources to go it alone. In that way, they can still reap the benefits of the bought in expertise while leveraging internal competitive advantage by defining a tailored cost model. Adopting a numerical optimization approach to the collateral allocation combined with a full economic cost model can be considered to be true collateral optimization and we believe that organizations that adopt this approach will gain significant competitive advantage through the coming collateral storm. 5

8 Choosing a path The approach and complexity involved in solving the disparate approaches to optimization vary wildly. A sequential process needs little mathematical knowledge and a team of application developers, or even proficient users of desktop spread sheet packages would be able to implement a solution. In contrast, a numerical mathematical approach to collateral optimization will deliver much greater benefits but requires time, money and highly specialized expertise. Deciding upon the correct path to take from the outset of your optimization journey is essential. A solution implementing a sequential approach will be unable to tackle the more advanced cost based method, whilst a cost based model can, through configuration, also be used to solve a sequential optimization. Collateral Optimization is one of many tools that firms can employ to address the fast growing cost of collateralizing the business. Key to success for an organization is in setting down the key goals for such a process and implementing a solution that provides a pragmatic approach for the short term with the potential to expand in scope and complexity as the business need unfolds. Figure 3. Collateral optimization requires sophisticated cost models and allocation methods Full economic cost model Dynamic ranking e.g. borrower activity rating Preference based ranking Cost model sophistication Allocation methods sophistication True collateral optimization Waterfall Numerical optimization Non-linear optimization 6 Collateral optimization how it really works

9 About SunGard s Apex Collateral SunGard s Apex Collateral solution helps collateral traders, heads of trading desks, risk professionals, operations staff and senior management manage and optimize their collateral on an enterprise-wide basis. Apex Collateral offers a single platform for trading directly from a real-time, consolidated global inventory as well as supporting operational requirements for underlying securities lending, repo and derivative transactions. It uses numerical algorithms to automatically allocate collateral in the optimal way, helping firms minimize costs and maximize return on assets. For more information, please visit: Contact us apexcollateral@sungard.com About SunGard SunGard is one of the world s leading software and technology services companies. SunGard serves approximately 25,000 customers in more than 70 countries and has approximately 17,000 employees. SunGard provides software and processing solutions for financial services, education and the public sector. SunGard also provides disaster recovery services, managed IT services, information availability consulting services and business continuity management software. With annual revenue of over $4.0 billion, SunGard is one of the largest privately held IT software and services companies SunGard. Trademark Information: SunGard, the SunGard and Intellimatch are trademarks or registered trademarks of SunGard Data Systems Inc. or its subsidiaries in the U.S. and other countries. All other trade names are trademarks or registered trademarks of their respective holders.